Object recognition in photos

We have solutions for object recognition and classification in photos or video.

Our technologies can be used for content moderation and classification, image tagging, identification of manufacturing defects etc.

Evaluation of shelf display in stores

We can analyse the shelf display in the stores through photographs. The system will evaluate the range of products identified and collect statistics on specific brands or SKUs present on the shelves.

Approximate record matching and fuzzy lookup

We can find and consolidate fuzzily duplicated records to simplify the work of editors and analysts.

Technological process optimisation to reduce manufacturing defects

At a production facility where a complex technological process is used, the result depends on the temperature, composition, and properties of the components as well as the start and stop times of steps of the production cycle.

In cooperation with production engineers, we will implement a system for monitoring the technological process, which provides real-time recommendations on changing the production parameters to minimise defective articles.

Churn prediction

If you provide subscription-based services, or users regularly return for purchases, we can collect the data and build a model that predicts the probability of a user's canceling the subscription. These results will enable you to process this target segment directly leading to a reduced customer churn.

Automation of marketing reports

Marketing departments often spend much time collecting data from different sources, including databases, CRM tools, Google Analytics and Excel files.

We will help assist in curating all the data into one database, normalize it, restructure it to an analytical form, and develop interactive reports featuring valuable business metrics.

Our process

Build a simple model

We implement a simplified model for solving the problem (using PyTorch or Keras), which allows for the analisys without the risks associated with implementing unproven technical solutions.The stage takes place at our facilities.

Improve the quality of the model

We iteratively enhance the quality of the model while generating and testing hypotheses that will help improve the effectiveness of training. We ultimately select the optimal model architecture, the most effective way of preparing the data, and the training process.

We document the work on each hypothesis to preserve the knowledge gained and to guide informed decisions.

This stage can occur at our facility or yours.

Launch full-scale operation

We migrate the model into Tensorflow, prepare production servers, and set up processes for quality control and regular updates of the model incorporating new data.

When solutions require working with big data, we deploy the Hadoop stack (HDFS, Hive, Spark) at your facility or through Amazon AWS.